In neuroscience and machine learning, three-factor learning is the combination of Hebbian plasticity with a third modulatory factor to stabilise and enhance synaptic learning. This third factor can represent various signals such as reward, punishment, error, surprise, or novelty, often implemented through neuromodulators. == Description == Three-factor learning introduces the concept of eligibility traces, which flag synapses for potential modification pending the arrival of the third factor, and helps temporal credit assignement by bridging the gap between rapid neuronal firing and slower behavioral timescales, from which learning can be done. Biological basis for Three-factor learning rules have been supported by experimental evidence. This approach addresses the instability of classical Hebbian learning by minimizing autocorrelation and maximizing cross-correlation between inputs.
Memory color effect
The memory color effect is the phenomenon that the canonical hue of a type of object acquired through experience (e.g. the sky, a leaf, or a strawberry) can directly modulate the appearance of the actual colors of objects. Human observers acquire memory colors through their experiences with instances of that type. For example, most human observers know that an apple typically has a reddish hue; this knowledge about the canonical color which is represented in memory constitutes a memory color. As an example of the effect, normal human trichromats, when presented with a gray banana, often perceive the gray banana as being yellow - the banana's memory color. In light of this, subjects typically adjust the color of the banana towards the color blue - the opponent color of yellow - when asked to adjust its surface to gray to cancel the subtle activation of banana's memory color. Subsequent empirical studies have also shown the memory color effect on man-made objects (e.g. smurfs, German mailboxes), the effect being especially pronounced for blue and yellow objects. To explain this, researchers have argued that because natural daylight shifts from short wavelengths of light (i.e., bluish hues) towards light of longer wavelengths (i.e., yellowish-orange hues) during the day, the memory colors for blue and yellow objects are recruited by the visual system to a higher degree to compensate for this fluctuation in illumination, thereby providing a stronger memory color effect. == Form identification == Memory color plays a role when detecting an object. In a study where participants were given objects, such as an apple, with two alternate forms for each, a crooked apple and a circular apple, researchers changed the colors of the alternate forms and asked if they could identify them. Most of the participants answered "unsure," suggesting that we use memory color when identifying an object. The research redefined memory color as a phenomenon when "a form's identity affects the phenomenal hue of that form." == Color effect on memorization == Memory color effect can be derived from the human instinct to memorize objects better. Comparing the effect of recognizing gray-scaled images and colored images, results showed that people were able to recall colored images 5% higher compared to gray-scaled images. An important factor was that higher level of contrast between the object and background color influences memory. In a specific study related to this, participants reported that colors were 5% to 10% easier to recognize compared to black and white. == Color constancy and memory color effect == Color constancy is the phenomenon where a surface to appear to be of the same color under a wide rage of illumination. A study tested two hypotheses with regards to color memory; the photoreceptor hypothesis and the surface reflectance hypothesis. The test color was surround either by various color patches forming a complex pattern or a uniform “grey” field at the same chromaticity as that of the illuminant. The test color was presented on a dark background for the control group. It was observed that complex surround results where in line with the surface-reflectance hypothesis and not the photoreceptor hypothesis, showing that the accuracy and precision of color memory are fundamentals to understanding the phenomenon of color constancy. == Significance to the evolution of trichromacy == While objects that possess canonical hues make up a small percentage of the objects which populate humans’ visual experience, the human visual system evolved in an environment populated with objects that possess canonical hues. This suggests that the memory color effect is related to the emergence of trichromacy because it has been argued that trichromacy evolved to optimize the ability to detect ripe fruits—objects that appear in canonical hues. == In perception research == In perception research, the memory color effect is cited as evidence for the opponent color theory, which states that four basic colors can be paired with its opponent color: red—green, blue—yellow. This explains why participants adjust the ripe banana color to a blueish tone to make its memory color yellow as gray. Researchers have also found empirical evidence that suggests memory color is recruited by the visual system to achieve color constancy. For example, participants had a lower percentage of color constancy when looking at a color incongruent scene, such as a purple banana, compared to a color diagnostical scene, a yellow banana. This suggests that color constancy is influenced by the color of objects that we are familiar with, which the memory color effect takes part.
Civitai
Civitai is an online platform and marketplace for generative artificial intelligence (Gen AI) content, primarily focused on AI-generated images and models, and AI-generated videos. == History == Civitai was founded in 2022 by Justin Maier. By January 2023, the site reached 100,000 registered users and 3 million by November. In November 2023, Civitai secured funding from venture capital firm Andreessen Horowitz. By April 2024, Civitai had 23.2 million monthly accesses. The company is headquartered in Boise, Idaho. == Platform == Civitai allows users to share and download AI models, particularly those used for image generation. The platform supports various AI models, including Stable Diffusion and Flux, and provides a space for users to showcase and monetize their AI-generated content. Users have profile pages and can comment on other users' models and images. The website also features a virtual currency called Buzz that can be used to generate images on Civitai's servers. Buzz can be bought or earned by engaging with the site. The platform is open source. == Controversies == In 2023, 404 Media reported that Civitai began a "Bounties" marketplace where users could commission deepfakes, of real or fake people. Users are rewarded with Buzz for completing Bounties. In December 2023, AI provider OctoML announced it had ended its business relationship with Civitai after concerns were raised users were generating images that “could be categorized as child pornography.”
Knowledge value chain
A knowledge value chain is a sequence of intellectual tasks by which knowledge workers build their employer's unique competitive advantage and/or social and environmental benefit. As an example, the components of a research and development project form a knowledge value chain. Productivity improvements in a knowledge value chain may come from knowledge integration in its original sense of data systems consolidation. Improvements also flow from the knowledge integration that occurs when knowledge management techniques are applied to the continuous improvement of a business process or processes. The term first started coming into common use around 1999, appearing in management-related talks and papers. It was registered as a trademark in 2004 by TW Powell Co., a Manhattan company. Knowledge value chain processes Knowledge acquisition Knowledge storage Knowledge dissemination Knowledge application
Civitai
Civitai is an online platform and marketplace for generative artificial intelligence (Gen AI) content, primarily focused on AI-generated images and models, and AI-generated videos. == History == Civitai was founded in 2022 by Justin Maier. By January 2023, the site reached 100,000 registered users and 3 million by November. In November 2023, Civitai secured funding from venture capital firm Andreessen Horowitz. By April 2024, Civitai had 23.2 million monthly accesses. The company is headquartered in Boise, Idaho. == Platform == Civitai allows users to share and download AI models, particularly those used for image generation. The platform supports various AI models, including Stable Diffusion and Flux, and provides a space for users to showcase and monetize their AI-generated content. Users have profile pages and can comment on other users' models and images. The website also features a virtual currency called Buzz that can be used to generate images on Civitai's servers. Buzz can be bought or earned by engaging with the site. The platform is open source. == Controversies == In 2023, 404 Media reported that Civitai began a "Bounties" marketplace where users could commission deepfakes, of real or fake people. Users are rewarded with Buzz for completing Bounties. In December 2023, AI provider OctoML announced it had ended its business relationship with Civitai after concerns were raised users were generating images that “could be categorized as child pornography.”
WeChat or Weixin in Chinese (Chinese: 微信; pinyin: Wēixìn ; lit. 'micro-message') is an instant messaging, social media, and mobile payment app developed by Tencent. First released in 2011, it became the world's largest standalone mobile app in 2018 with over 1 billion monthly active users. The Chinese version of WeChat, Weixin, has been described as China's "app for everything" and a super-app because of its wide range of functions. WeChat provides text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video conferencing, video games, mobile payment, sharing of photographs and videos and location sharing. It has been described as having "an almost indispensable part of life in China". Accounts registered using Chinese phone numbers are managed under the Weixin brand, and their data is stored in mainland China and subject to Weixin's terms of service and privacy policy. Non-Chinese numbers are registered under WeChat, and WeChat users are subject to a more liberal terms of service and better privacy policy, and their data is stored in the Netherlands for users in the European Union, and in Singapore for other users. User activity on Weixin, the Chinese version of the app, is analyzed, tracked and shared with Chinese authorities upon request as part of the mass surveillance network in China. Chinese-registered Weixin accounts censor politically sensitive topics, and the software license agreement for Weixin (but not WeChat) explicitly forbids content which "[en]danger[s] national security, divulge[s] state secrets, subvert[s] state power and undermine[s] national unity", as well as other types of content such as content that "[u]ndermine[s] national religious policies" and content that is "[i]nciting illegal assembly, association, procession, demonstrations and gatherings disrupting the social order". Due to its central part of Chinese life, a Chinese person having their WeChat account banned can cause a significant disruption to their life. Any interactions between Weixin and WeChat users are subject to the terms of service and privacy policies of both services. == History == By 2010, Tencent had already attained a massive user base with their desktop messenger app QQ. Recognizing smart phones were likely to disrupt this status quo, CEO Pony Ma sought to proactively invest in alternatives to their own QQ messenger app. WeChat began as a project at Tencent Guangzhou Research and Project center in October 2010. The original version of the app was created by Allen Zhang, named "Weixin" (微信) by Pony Ma, and launched in 2011. The user adoption of WeChat was initially very slow, with users wondering why key features were missing; however, after the release of the Walkie-talkie-like voice messaging feature in May of that year, growth surged. By 2012, when the number of users reached 100 million, Weixin was re-branded "WeChat" by President Martin Lau for the international market. During a period of government support of e-commerce development—for example in the 12th five-year plan (2011–2015)—WeChat also saw new features enabling payments and commerce in 2013, which saw massive adoption after their virtual Red envelope promotion for Chinese New Year 2014. WeChat had over 889 million monthly active users by 2016, and as of 2019 WeChat's monthly active users had risen to an estimate of one billion. As of January 2022, it was reported that WeChat has more than 1.2 billion users. After the launch of WeChat payment in 2013, its users reached 400 million the next year, 90 percent of whom were in China. By comparison, Facebook Messenger and WhatsApp had about one billion monthly active users in 2016 but did not offer most of the other services available on WeChat. For example, in Q2 2017, WeChat's revenues from social media advertising were about US$0.9 billion (RMB6 billion) compared with Facebook's total revenues of US$9.3 billion, 98% of which were from social media advertising. WeChat's revenues from its value-added services were US$5.5 billion. By 2018, WeChat had been used by 93.5% of Chinese internet users. In that year, it became the world's largest standalone mobile app in 2018 with over 1 billion monthly active users. In response to a border dispute between India and China, WeChat was banned in India in June 2020 along with several other Chinese apps, including TikTok. U.S. president Donald Trump sought to ban U.S. "transactions" with WeChat through an executive order but was blocked by a preliminary injunction issued in the United States District Court for the Northern District of California in September 2020. Joe Biden officially dropped Trump's efforts to ban WeChat in the U.S. in June 2021. == Features == WeChat, has been described as China's "app for everything" and a super-app because of its wide range of functions. WeChat provides text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video conferencing, video games, mobile payment, sharing of photographs and videos and location sharing. It has been described as having "an almost indispensable part of life in China". Due to its central part of Chinese life, a Chinese person having their WeChat account banned can cause a significant disruption to their life. === Messaging === WeChat provides a variety of features including text messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video calls and conferencing, video games, photograph and video sharing, as well as location sharing. WeChat also allows users to exchange contacts with people nearby via Bluetooth, as well as providing various features for contacting people at random if desired (if people are open to it). It can also integrate with other social networking services such as Facebook and Tencent QQ. Photographs may also be embellished with filters and captions, and automatic translation service is available and could also translate the conversation during messaging. WeChat supports different instant messaging methods, including text messages, voice messages, walkie talkie, and stickers. Users can send previously saved or live pictures and videos, profiles of other users, coupons, lucky money packages, or current GPS locations with friends either individually or in a group chat. WeChat also provides a message recall feature to allow users to recall and withdraw information (e.g. images, documents) that are sent within 2 minutes in a conversation. WeChat also provides a voice-to-text feature that brings convenience when it is not convenient to listen to voice messages, as well as the basic ability to recognize emojis based on different tones of voice. A distance sensing feature is implemented in WeChat. It has the ability to activate the receivers' hold-to-talk function when the phone was brought in close proximity to the ear. After the receiver was held at a certain distance from the ear, the sensor would then proceed to automatically disable the phone speakers. This feature eliminates the risk of the user's voice messages being inadvertently broadcast to the general public. === Public accounts === WeChat users can register as a public account (公众号), which enables them to push feeds to subscribers, interact with subscribers, and provide subscribers with services. Users can also create an official account, which fall under service, subscription, or enterprise accounts. Once users as individuals or organizations set up a type of account, they cannot change it to another type. By the end of 2014, the number of WeChat official accounts had reached 8 million. Official accounts of organizations can apply to be verified (cost 300 RMB or about US$45). Official accounts can be used as a platform for services such as hospital pre-registrations, or credit card service. To create an official account, the applicant must register with Chinese authorities, which discourages "foreign companies". In April 2022, WeChat announced that it will start displaying the location of users in China every time they post on a public account. Meanwhile, overseas users on public accounts will also display the country based on their IP address. === Moments === "Moments" (朋友圈) is WeChat's brand name for its social feed of friends' updates. "Moments" is an interactive platform that allows users to post images, text, and short videos taken by users. It also allows users to share articles and music (associated with QQ Music or other web-based music services). Friends in the contact list can like the content and leave comments, functioning similarly to a private social network. In 2017 WeChat had a policy of a maximum of two advertisements per day per Moments user. Privacy in WeChat works by groups of friends: only the friends from the user's contact are able to view their Moments' contents and comments. The friends of the user will only be able to see the likes and comments from other users only if they are in a mutual friend group. For example, friends from high school are not able to
Strategic Computing Initiative
The United States government's Strategic Computing Initiative funded research into advanced computer hardware and artificial intelligence from 1983 to 1993. The initiative was designed to support various projects that were required to develop machine intelligence in a prescribed ten-year time frame, from chip design and manufacture, computer architecture to artificial intelligence software. The Department of Defense spent a total of $1 billion on the project. The inspiration for the program was Japan's fifth generation computer project, an enormous initiative that set aside billions for research into computing and artificial intelligence. As with Sputnik in 1957, the American government saw the Japanese project as a challenge to its technological dominance. The British government also funded a program of their own around the same time, known as Alvey, and a consortium of U.S. companies funded another similar project, the Microelectronics and Computer Technology Corporation. The goal of SCI, and other contemporary projects, was nothing less than full machine intelligence. "The machine envisioned by SC", according to Alex Roland and Philip Shiman, "would run ten billion instructions per second to see, hear, speak, and think like a human. The degree of integration required would rival that achieved by the human brain, the most complex instrument known to man." The initiative was conceived as an integrated program, similar to the Apollo moon program, where different subsystems would be created by various companies and academic projects and eventually brought together into a single integrated system. Roland and Shiman wrote that "While most research programs entail tactics or strategy, SC boasted grand strategy, a master plan for an entire campaign." The project was funded by the Defense Advanced Research Projects Agency and directed by the Information Processing Technology Office (IPTO). By 1985 it had spent $100 million, and 92 projects were underway at 60 institutions: half in industry, half in universities and government labs. Robert Kahn, who directed IPTO in those years, provided the project with its early leadership and inspiration. Clint Kelly managed the SC Initiative for three years and developed many of the specific application programs for DARPA, such as the Autonomous Land Vehicle. By the late 1980s, it was clear that the project would fall short of realizing the hoped-for levels of machine intelligence. Program insiders pointed to issues with integration, organization, and communication. When Jack Schwarz ascended to the leadership of IPTO in 1987, he cut funding to artificial intelligence research (the software component) "deeply and brutally", "eviscerating" the program (wrote Pamela McCorduck). Schwarz felt that DARPA should focus its funding only on those technologies which showed the most promise. In his words, DARPA should "surf", rather than "dog paddle", and he felt strongly AI was not "the next wave". The project was superseded in the 1990s by the Accelerated Strategic Computing Initiative and then by the Advanced Simulation and Computing Program. These later programs did not include artificial general intelligence as a goal, but instead focused on supercomputing for large scale simulation, such as atomic bomb simulations. The Strategic Computing Initiative of the 1980s is distinct from the 2015 National Strategic Computing Initiative—the two are unrelated. == Results == Although the program failed to meet its goal of high-level machine intelligence, it did meet some of its specific technical objectives, for example those of autonomous land navigation. The Autonomous Land Vehicle program and its sister Navlab project at Carnegie Mellon University, in particular, laid the scientific and technical foundation for many of the driverless vehicle programs that came after it, such as the Demo II and III programs (ALV being Demo I), Perceptor, and the DARPA Grand Challenge. The use of video cameras plus laser scanners and inertial navigation units pioneered by the SCI ALV program form the basis of almost all commercial driverless car developments today. It also helped to advance the state of the art of computer hardware to a considerable degree. On the software side, the initiative funded development of the Dynamic Analysis and Replanning Tool (DART), a program that handled logistics using artificial intelligence techniques. This was a huge success, saving the Department of Defense billions during Desert Storm. Introduced in 1991, DART had by 1995 offset the monetary equivalent of all funds DARPA had channeled into AI research for the previous 30 years combined.